Companies daily need to optimize their products, hence optimization plays a significant role in today's design cycle. Problems related to one or more than one objective, originate in several ...

Companies daily need to optimize their products, hence optimization plays a significant role in today's design cycle. Problems related to one or more than one objective, originate in several disciplines; typically using a single optimization technology is not sufficient to deal with real-life problems, particularly when the design concerns complex and expensive products. Therefore, engineers are frequently asked to solve problems with several conflicting objective functions. The multiobjective optimization approach provides a set of non-dominant designs (Pareto optimality) where a further improvement for one objective is at the expense of all the others: this allows designers to choose the best solution for each scenario.

Solving real-world multiobjective problems is not simple, engineers must address problems connected to the non-linearity of the functions, complexity of the physics and the computational cost that snowballs as the number of parameters increases. Moreover, the coupling between disciplines for design a product can be really challenging, involving several complicating factors, such as the limitation on the computational resources, and even a lack of communication between different departments.

This tutorial is a survey on methodologies to approach design optimization process, a set of best practices intended for rapid delivery of high-quality products, with a specific focus on the numerical algorithms and post-processing used for selecting optimal design configurations.

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